{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\hpcq_\\.conda\\envs\\cvxpyenv\\lib\\site-packages\\arctic-1.80.0-py3.8.egg\\arctic\\store\\_pandas_ndarray_store.py:8: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n"
     ]
    }
   ],
   "source": [
    "from arctic import CHUNK_STORE, Arctic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "a = Arctic('localhost')\n",
    "# a.initialize_library('fund', lib_type=CHUNK_STORE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "fund = a['fund']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "3799"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "len( fund.list_symbols() )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'chunk_count': 17,\n",
       " 'len': 508,\n",
       " 'appended_rows': 7,\n",
       " 'metadata': {'columns': ['date', 'weeklyReturn', 'dailyProfit']},\n",
       " 'chunker': 'date',\n",
       " 'chunk_size': 'M',\n",
       " 'serializer': 'FrameToArray'}"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "# lib.delete( '000001' )\n",
    "fund.get_info('000198') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = datetime.date(2020, 5, 7)\n",
    "end = datetime.date(2021, 5, 7)\n",
    "\n",
    "request_dates = pd.date_range(start, end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = fund.read( '000198', chunk_range=request_dates )\n",
    "df = fund.read( '000198' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(508, 2)"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "chunk_range = pd.date_range('2021-05-15', '2021-05-21')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "fund.delete('000198',chunk_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = fund.read('000198')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('temp/000198.csv', index_col=0, parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index.name = 'date'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "fund.append( '000198', df, upsert=True, chunk_size='M' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = fund.read('000198')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "            weeklyReturn  dailyProfit\n",
       "date                                 \n",
       "2021-05-02         2.130       0.5768\n",
       "2021-05-03         2.129       0.5768\n",
       "2021-05-04         2.129       0.5767\n",
       "2021-05-05         2.128       0.5767\n",
       "2021-05-06         2.128       0.5779\n",
       "2021-05-07         2.126       0.5729\n",
       "2021-05-08         2.131       0.5862\n",
       "2021-05-09         2.130       0.5745\n",
       "2021-05-10         2.130       0.5764\n",
       "2021-05-11         2.126       0.5696\n",
       "2021-05-12         2.124       0.5734\n",
       "2021-05-13         2.121       0.5718\n",
       "2021-05-14         2.121       0.5738\n",
       "2021-05-15         2.112       0.5693\n",
       "2021-05-16         2.109       0.5691\n",
       "2021-05-17         2.104       0.5671\n",
       "2021-05-18         2.099       0.5602\n",
       "2021-05-19         2.092       0.5588\n",
       "2021-05-20         2.084       0.5569\n",
       "2021-05-21         2.075       0.5572"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>weeklyReturn</th>\n      <th>dailyProfit</th>\n    </tr>\n    <tr>\n      <th>date</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2021-05-02</th>\n      <td>2.130</td>\n      <td>0.5768</td>\n    </tr>\n    <tr>\n      <th>2021-05-03</th>\n      <td>2.129</td>\n      <td>0.5768</td>\n    </tr>\n    <tr>\n      <th>2021-05-04</th>\n      <td>2.129</td>\n      <td>0.5767</td>\n    </tr>\n    <tr>\n      <th>2021-05-05</th>\n      <td>2.128</td>\n      <td>0.5767</td>\n    </tr>\n    <tr>\n      <th>2021-05-06</th>\n      <td>2.128</td>\n      <td>0.5779</td>\n    </tr>\n    <tr>\n      <th>2021-05-07</th>\n      <td>2.126</td>\n      <td>0.5729</td>\n    </tr>\n    <tr>\n      <th>2021-05-08</th>\n      <td>2.131</td>\n      <td>0.5862</td>\n    </tr>\n    <tr>\n      <th>2021-05-09</th>\n      <td>2.130</td>\n      <td>0.5745</td>\n    </tr>\n    <tr>\n      <th>2021-05-10</th>\n      <td>2.130</td>\n      <td>0.5764</td>\n    </tr>\n    <tr>\n      <th>2021-05-11</th>\n      <td>2.126</td>\n      <td>0.5696</td>\n    </tr>\n    <tr>\n      <th>2021-05-12</th>\n      <td>2.124</td>\n      <td>0.5734</td>\n    </tr>\n    <tr>\n      <th>2021-05-13</th>\n      <td>2.121</td>\n      <td>0.5718</td>\n    </tr>\n    <tr>\n      <th>2021-05-14</th>\n      <td>2.121</td>\n      <td>0.5738</td>\n    </tr>\n    <tr>\n      <th>2021-05-15</th>\n      <td>2.112</td>\n      <td>0.5693</td>\n    </tr>\n    <tr>\n      <th>2021-05-16</th>\n      <td>2.109</td>\n      <td>0.5691</td>\n    </tr>\n    <tr>\n      <th>2021-05-17</th>\n      <td>2.104</td>\n      <td>0.5671</td>\n    </tr>\n    <tr>\n      <th>2021-05-18</th>\n      <td>2.099</td>\n      <td>0.5602</td>\n    </tr>\n    <tr>\n      <th>2021-05-19</th>\n      <td>2.092</td>\n      <td>0.5588</td>\n    </tr>\n    <tr>\n      <th>2021-05-20</th>\n      <td>2.084</td>\n      <td>0.5569</td>\n    </tr>\n    <tr>\n      <th>2021-05-21</th>\n      <td>2.075</td>\n      <td>0.5572</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 32
    }
   ],
   "source": [
    "df2.tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2020-05-07        NaN\n",
       "2020-05-08        NaN\n",
       "2020-05-09        NaN\n",
       "2020-05-10        NaN\n",
       "2020-05-11        NaN\n",
       "               ...   \n",
       "2021-05-03    11.7247\n",
       "2021-05-04    11.7106\n",
       "2021-05-05    11.6882\n",
       "2021-05-06    11.6709\n",
       "2021-05-07    11.6482\n",
       "Name: dailyProfit, Length: 366, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dailyProfit.rolling(window=20, center=False).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkdf = pd.concat([ df.dailyProfit, df.dailyProfit.rolling(window=20, center=False).sum()], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkdf.to_csv('check.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fundopt.fundtsloader import getTSLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = getTSLoader('000198')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "loader.load(start, end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0011212200000000002"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loader.getReturnByDate(datetime.date(2020, 5, 10), datetime.date(2020, 6,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-05-07    NaN\n",
       "2020-05-08    NaN\n",
       "2020-05-11    NaN\n",
       "2020-05-12    NaN\n",
       "2020-05-13    NaN\n",
       "             ... \n",
       "2021-05-03    0.0\n",
       "2021-05-04    0.0\n",
       "2021-05-05    0.0\n",
       "2021-05-06    0.0\n",
       "2021-05-07    0.0\n",
       "Freq: B, Name: return, Length: 262, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loader.getReturnTS(start, end, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2021-05-08 00:00:00')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2020-01-01 00:00:00')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(494, 2)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = fund.read('000198')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2021-05-15 00:00:00')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = fund.read('003064', chunk_range=pd.bdate_range('2020-05-15','2021-05-15'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = getTSLoader('003064')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = datetime.date(2020, 1, 1)\n",
    "end = datetime.date(2021, 5, 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "loader.load(start, end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "loader.getReturnTS(start, end, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ff90eacdc8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.ACC_NAV.diff().plot.hist(bins = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ff92d48c08>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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